text: '''
  A shallow CNN to see if the classifier's working
  
  Approach 1.
  For more details, see the IPython notebook Stanford_Dogs.ipynb in the ipython_notebooks directory.
  '''
  
  from keras.preprocessing.image import ImageDataGenerator
  from keras.models import Sequential
  from keras.layers import Conv2D, MaxPooling2D
  from keras.layers import Activation, Dropout, Flatten, Dense
  from keras import backend as K
  
  
  # dimensions of our images.
  img_width, img_height = 200, 200
  
  train_data_dir = '../data/keras_small_ds/train'
  validation_data_dir = '../data/keras_small_ds/validation'
  nb_train_samples = 8400
  nb_validation_samples = 3600
  epochs = 5
  batch_size = 16
  nb_classes = 120
  
  if K.image_data_format() == 'channels_first':
      input_shape = (3, img_width, img_height)
  else:
      input_shape = (img_width, img_height, 3)
  
  model = Sequential()
  # model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))
  model.add(Conv2D(32, (3, 3), input_shape=input_shape))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  # model.add(Conv2D(32, (3, 3), padding='same'))
  model.add(Conv2D(32, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  # model.add(Conv2D(64, (3, 3), padding='same'))
  model.add(Conv2D(64, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  
  model.add(Flatten())
  model.add(Dense(64))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(nb_classes))
  model.add(Activation('softmax'))
  # model.add(Activation('sigmoid'))
  
  model.compile(loss='categorical_crossentropy',
                optimizer='rmsprop',
                metrics=['accuracy'])
  
  # augmentation configuration for training
  train_datagen = ImageDataGenerator(
      rescale=1. / 255,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True)
  
  # augmentation configuration for testing:
  # only rescaling
  test_datagen = ImageDataGenerator(rescale=1. / 255)
  
  train_generator = train_datagen.flow_from_directory(
      train_data_dir,
      target_size=(img_width, img_height),
      batch_size=batch_size,
      class_mode='categorical')
  
  validation_generator = test_datagen.flow_from_directory(
      validation_data_dir,
      target_size=(img_width, img_height),
      batch_size=batch_size,
      class_mode='categorical')
  
  model.fit_generator(
      train_generator,
      steps_per_epoch=nb_train_samples // batch_size,
      epochs=epochs,
      validation_data=validation_generator,
      validation_steps=nb_validation_samples // batch_size)
  
  model.save_weights('../models/weights/first_try.h5')
  